Summary of Shapley Values-powered Framework For Fair Reward Split in Content Produced by Genai, By Alex Glinsky et al.
Shapley Values-Powered Framework for Fair Reward Split in Content Produced by GenAI
by Alex Glinsky, Alexey Sokolsky
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a method for fairly assessing the contributions of human professionals in training generative models, particularly in scenarios where AI surpasses human quality. The authors highlight the impending social upheaval caused by the gap between human and AI skills, emphasizing the need to quantify the value of human contributions in collaborative tasks. To achieve this, they employ Shapley Values to calculate the contribution of artists in an image generated by the Stable Diffusion-v1.5 model. This framework structures collaboration between model developers and data providers, ensuring equitable compensation for human professionals as AI replaces certain tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper thinks about a future where machines can do things better than humans. Right now, humans are really good at making art, music, and writing, but soon machines might be able to do these same things. To make sure people who have spent years learning their craft aren’t left behind, the authors came up with a way to measure how much each person contributes when working together with machines. They use special math formulas to figure out what’s fair for everyone involved. This is important because it helps us think about how we can help those affected by this change in the future. |
Keywords
» Artificial intelligence » Diffusion
